Predicting Apartment Prices using Machine Learning

📂 Description

In this project, I applied Machine Learning techniques to estimate the fair rental value of properties in the city of São Paulo. I used a public dataset containing real information about properties from April 2019. I organized the data analysis and modeling workflow, from data collection and cleaning to the creation and evaluation of predictive models. The scripts I developed transformed raw data into valuable insights, enabling property price predictions and providing practical solutions to a real-world problem.

💻 Running the application

cd predicting-apartment-prices
jupyter notebook Predicting_Apartment_Prices_using_Machine_Learning.ipynb

📝 Project Structure

predicting-apartment-price/
│
├── gPredicting_Apartment_Prices_using_Machine_Learning.ipynb    # Jupyter notebook for analyzing
└── Datasets/                                                    # Folder containing the datasets
    ├── sao-paulo-properties-april-2019.csv                      # CSV file

📚 Languages, dependencies, and libs used

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